An ensemble of support vector regressors over embedding-space neighbors reaches recall 0.849 on German legal passage retrieval, higher than the reported GerDaLIR baselines.
JNLP Team: Deep Learning for Legal Processing in COLIEE 2020
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abstract
We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020. These systems are all characterized by being pre-trained on large amounts of data before being finetuned for the specified tasks. This approach helps to overcome the data scarcity and achieve good performance, thus can be useful for tackling related problems in information retrieval, and decision support in the legal domain. Besides, the approach can be explored to deal with other domain specific problems.
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Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval via Bagging and SVR Ensembles
An ensemble of support vector regressors over embedding-space neighbors reaches recall 0.849 on German legal passage retrieval, higher than the reported GerDaLIR baselines.